{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trade with XGBoost algorithm\n",
    "### Background\n",
    "In the [portfolio trade example](https://github.com/rapidsai/gQuant/blob/master/notebooks/04_portfolio_trade.ipynb), we use gQuant to backtest a simple mean reversion trading strategy on 5000 stocks.\n",
    "It shows decent performance by tweaking the moving average window size. Searching for alpha signal is the ultimate goal for the trading companies. A lot of different methods are used to do so. Machine learning approach\n",
    "is one of those. It has the benefits of extracting important information in the data automatically given enough computation. There are a few popular machine learning algrithoms, including SVM, Random forest tree etc. Amoung those, XGBoost is known to be a very powerful machine \n",
    "learning method that is winning a lot of [ML competitions](https://medium.com/syncedreview/tree-boosting-with-xgboost-why-does-xgboost-win-every-machine-learning-competition-ca8034c0b283). Luckily, the [RAPIDS library](https://github.com/rapidsai) accelerates the XGBoost ML algorithm in the GPU so that we can easily take advantage of it in the gQuant. \n",
    "\n",
    "In this notebook, we are going to demo how to use gQuant to backtest a XGBoost based trading stragty.\n",
    "\n",
    "\n",
    "### Environment Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys; sys.path.insert(0, '..')\n",
    "\n",
    "import warnings\n",
    "from gquant.dataframe_flow import TaskGraph\n",
    "import ipywidgets as widgets\n",
    "import os\n",
    "\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some constants for the data filters. If using GPU of 32G memory, you can safely set the min_volume to 5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_volume = 400.0\n",
    "min_rate = -10.0\n",
    "max_rate = 10.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, this notebook requires `cudf` of version >=0.8.0. It can be checked by following command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13.0a+4804.g6158033.dirty\n"
     ]
    }
   ],
   "source": [
    "import cudf\n",
    "print(cudf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The toy example\n",
    "To mimic the end-to-end quantitative analyst task, we are going to backtest a XGBoost trading strategy. The workflow includes following steps:\n",
    "\n",
    "1. Load the 5000 end-of-day stocks CSV data into the dataframe\n",
    "\n",
    "2. Add rate of return feature to the dataframe.\n",
    "\n",
    "3. Clean up the data by removing low volume stocks and extreme rate of returns stocks.\n",
    "\n",
    "4. Compute the features based on different technical indicators \n",
    "\n",
    "5. Split the data in training and testing and build a XGBoost model based on the training data. From the XGBoost model, compute the trading signals for all the data points.\n",
    "\n",
    "5. Run backtesting and compute the returns from this strategy for each of the days and stock symbols \n",
    "\n",
    "6. Run a simple portfolio optimization by averaging the stocks together for each of the trading days.\n",
    "\n",
    "7. Compute the sharpe ratio and cumulative return results for both training and testing datasets\n",
    "\n",
    "The whole workflow can be organized into a computation graph, which are fully described in a yaml file. Here is snippet of the yaml file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- id: node_csvdata\n",
      "  type: CsvStockLoader\n",
      "  conf:\n",
      "    path: ./data/stock_price_hist.csv.gz\n",
      "  inputs: []\n",
      "- id: node_sort\n",
      "  type: SortNode\n",
      "  conf:\n",
      "    keys:\n",
      "      - asset\n",
      "      - datetime\n",
      "  inputs:\n",
      "    - node_csvdata\n",
      "- id: node_addReturn\n",
      "  type: ReturnFeatureNode\n",
      "  conf: {}\n",
      "  inputs:\n",
      "    - node_sort\n",
      "- id: node_addIndicator\n",
      "  type: AssetIndicatorNode\n",
      "  conf: {}\n",
      "  inputs:\n",
      "    - node_addReturn\n",
      "- id: node_volumeMean\n",
      "  type: AverageNode\n",
      "  conf:\n",
      "    column: volume\n",
      "  inputs: \n",
      "    - node_addIndicator\n"
     ]
    }
   ],
   "source": [
    "!head -n 29 ../task_example/xgboost_trade.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each nodes has a unique id, a node type, configuration parameters and input nodes ids. gQuant takes this yaml file, wires it into a graph to visualize it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ADFsvvfSSlixZorvvvlvr16/X+PHjfV3SoKxatUo2m81ajhw5oltuuUXR0dGKi4vTggULdPDgwT7r1dfXa8WKFRo1apSCg4MVExOj6667TmvXru3Td9++fVq0aJGcTqfCw8M1ffp0rV+/fsCaamtrdf/992vEiBEKDg5WQkKCioqKVFxcPOTxXXXVVV7jW7ZsmaTP59s/tb2pqanfesPCwjR58mS9+eabXuvcdddd/Y5z/vz51nozZ87Uhg0b/qFjN9i+nZ2deuyxxzR+/HiFhYUpNjZWCxcu1OrVq62pT5555hnZbDa1trZqw4YN1liCgoKGfFzPxv6+eO6Vlpbq5ptvVlxcnNVWV1en7u5uvfrqq5ozZ46Sk5PlcDiUl5en5557Th6Px6ppsOMbyvk1mHPhpptu8hqHzWbTD37wA0mfT6F1avtNN9005GPtK7m5udqwYYO+9rWv6Z/+6Z/05z//2dclAQAAwJcMAADAMFRcXGxCQkLMt7/9bV+XcsZuvPFGI8nceOONZuPGjaalpcW89957xuFwmEmTJnn1raqqMtnZ2SYpKcmsWbPGuFwuU1paaoqKiozNZjO//vWvrb779+830dHRJi0tzbz77rvG7XabnTt3mmuvvdaMGDHChISEeG27srLSZGVlmaSkJPPWW28Zt9ttdu/ebQoLC01oaKjZuHHjkMdWXFxswsPDTX5+vmlpaTHGGNPR0WGmTJliXnnlFa++/dW7e/duM3v2bJOQkNCnXmOMyc/PN06n08ycOdOsX7/euN1us2XLFnPppZea4OBgs27dujM6dkPpe9dddxmn02neffdd09bWZqqrq81DDz1kJJm1a9d61RseHm6mTZs25ON4qrO5v95zr7Cw0Kxdu9a0traaTZs2mcDAQFNbW2vWrFljJJknnnjCNDQ0mNraWvPzn//cBAQEmIceeqjP9k63v6GcX0M9F+bOnWsCAgLMgQMH+uz3K1/5inn55ZdPd0j92je/+U0TFhZmSkpKfF0KAAAAfITwHgAADEs33HCDufLKK01PT4+vSzljvQHqmjVrvNpvuukmI8nU1tZabXfccYeR1Cf47ujoMKmpqcbhcJjq6mpjjDFLliwxksxrr73m1beiosKEhIT0CUCXL19uJPUJOquqqkxISIgpKCg4o/H97//+r5FkioqKjMfjMcuXLzf//u//3qffQPXW1NSYsLCwAcN7SeaTTz7xat+5c6eRZPLz8622oRy7ofTNzs42U6dO7VPb2LFjz0l4fzb313vuvf322/1+v2bNGjNjxow+7cuWLTN2u924XK5B728o59dQz4V33nnHSDL33XefV/v69etNWlqaOXnyZL81DQfd3d0mPz/ffPWrX/V1KQAAAPARps0BAADDTnd3t9555x194xvfUEDA8P9zZtKkSV6fMzIyJEmVlZVW2+uvvy5Jmj9/vlffkJAQzZo1S+3t7XrnnXckSX//+98lSXPnzvXqm5qaqrFjx/bZ/6pVqxQQEKAFCxZ4tScnJys3N1fbtm3T8ePHhzyuJUuW6Dvf+Y7++te/6qqrrlJ9fb0ef/zxPv0GqjchIeG0UyGFhoZqypQpXm15eXlKTU3Vjh07VFVVJWlox24ofefNm6eNGzfq61//ujZt2mRNXVNaWqoZM2YMfGDO0LnY3+TJk/ttX7BgQb9TCuXn56urq0t79uwZ9D6Gcn4N9Vy49tprlZeXp9/+9reqr6+32p9++ml985vflN1uH3Sd/iYwMFD33Xef3nrrLRljfF0OAAAAfGD4/98uAAC46LhcLnV2dio1NdXXpZwVTqfT63NwcLAkWXOLd3Z2yuVyKTQ0VJGRkX3WT0pKkiRVV1ers7NTbrdboaGhioiI6NM3MTHR63Pvtj0ej5xOZ595xLdv3y5J2r9//xmN7fHHH9eUKVO0ceNGLVmypM/Fli+rNyYmZsBt987T/kW9Y6ypqRnysRtsX0l64YUX9NJLL+nQoUOaNWuWoqKiNG/ePOsCwNl2LvYXHh7eb7vL5dJjjz2mvLw8xcTEWOfDww8/LElqa2sb1PaHcn6d6bnw4IMPqq2tTb/4xS8kSWVlZfrwww/19a9/fVA1+rO0tDS53e5BH28AAABcWAjvAQDAsBMXF6f4+Hh9+umnvi7lvAgJCZHT6VRHR4fcbnef70+cOCHp8zuZQ0JCFBkZqY6ODrW0tPTp29DQ0Gfb0dHRCgoKUldXl8zn0yr2WWbOnHlGta9bt04ul0t5eXm67777tGPHjj77P129NTU1A27b5XL12967TmJi4pCP3WD7SpLNZtNtt92m999/X01NTVq1apWMMSoqKtJPf/pTr3X7u8gwVOdzfwsXLtTjjz+uu+++W2VlZfJ4PDLG6Nlnn5WkPneCD7S/oZxfZ3ouLF26VElJSXr++efV2dmpn/zkJ1q+fPlpL/wMF5s2bVJGRsaAF1kAAABwYSO8BwAAw9Ldd9+tZ5991poa5UK3ePFiSdJbb73l1d7Z2akPPvhADofDmmrkuuuuk/T/piDpVVdXp9LS0j7bLioqUnd3tzZs2NDnux//+MfKzMxUd3f3kGs+fPiw7rzzTv3lL3/R6tWr5XA4dOONN6q2ttar30D1VldXq6ysbMDtt7S09LkYsGvXLlVWVio/P18pKSmShnbshtI3Ojpa+/btkyTZ7XbNmTNHq1atks1m67N+WFiYTp48aX0eN26cfvWrXw04tv6cr/319PRow4YNSk5O1v3336+EhAQrnG9vb+93ndPtbyjn15mcCyEhIbrvvvtUU1Ojn/zkJ3r55Zf1wAMPDGqs/uzYsWN6/vnnL4hfEAAAAOAMnfdZ9gEAAM6CpqYmM27cODN58mRTX1/v63LOSO9DQ9vb273aH3nkESPJfPbZZ1ZbVVWVyc7ONklJSWbNmjWmubnZlJaWmqKiImOz2cyvfvUrq++BAwdMbGysSUtLM++++65xu91mz549Zu7cuSYxMbHPQz9PnDhhRo0aZUaOHGnefvtt09TUZOrr683KlStNWFiYefXVV4c8NrfbbS699FLzxhtvWG3r1q0zdrvdXH311V4PEu2v3l27dpl58+aZrKysAR9YGx4ebq666iqzadMm09LSYrZs2WIuvfRSExwcbNatW3dGx24ofZ1OpyksLDQ7duwwHR0d5sSJE+Z73/uekWR+8IMfeNU7b94843Q6zbFjx8zGjRtNUFCQ2bt375CO6dnc30DnXq9rrrnGSDJPPfWUqa2tNW1tbebDDz80mZmZRpJ57733Br2/oZxfZ3IuGGNMbW2tcTgcxmazmRtvvHFIx9Uf1dTUmPz8fHPppZcat9vt63IAAADgI4T3AABg2CorKzMZGRlm3LhxQw5CfemTTz4xkryW73znO8YY06d9/vz51np1dXXmwQcfNNnZ2cZutxun02nmzp1rPvjggz77KC0tNYsWLTJRUVHG4XCYSZMmmTfffNPMmjXL2vadd95p9a+vrzcrVqwwI0eONHa73SQkJJhrr722T0g7GN/4xje8xrBr1y5TW1vbZ2yPP/54v/WGhYWZqVOnmo8++sjMmDHDhIWFWf2efvppa/20tDSzefNmM3PmTBMREWEcDocpLCw069ev71PTUI7dYPsWFxebe+65x1xyySUmLCzMxMbGmiuvvNL8+te/Nh6Px6vvvn37zPTp0014eLjJyMgwL7zwwpCP69nYX3/nXn/389TW1pp77rnHZGRkGLvdbpKSkswdd9xhHn30UWudgoKCQY9vKOfXYM+FL7r77ruNJPPRRx8N6bj6m+LiYjNy5EgzatQoc+TIEV+XAwAAAB+yGfOFCSsBAACGkaqqKhUVFam4uFj/8R//oRUrVlgPfMXwN378eLW3t+vo0aO+LgU+9mXnwosvvqgXXnhBW7duPc+VnR0dHR360Y9+pCeffFJXXnmlXnvtNSUkJPi6LAAAAPgQc94DAIBhLSUlRR9//LG+973v6T//8z81fvx4vfzyy/J4PL4uDYNUXV2t2NhYdXV1ebUfOXJEBw8e1DXXXOOjynC+/SPnwsqVK7VixYpzXeJZ193drRdffFFjx47VM888ox//+Mdau3YtwT0AAAAI7wEAwPAXFBSkRx55RGVlZZoxY4aWL1+usWPH6vnnn1dra6uvy8MgNDY26p577lF5ebna2tq0efNm3XLLLYqKitJ3v/tdX5eH82iw58JvfvMbLV68WC0tLVq5cqUaGxt18803+7DyoXG73Xr22Wc1ZswY3XPPPbr++utVVlamBx98UAEB/G8aAAAACO8BAMAFJCMjQ//zP/+jkpISzZkzR4888ojS0tJ07733asuWLb4ub9iz2Wxfunzve98b8naTk5P1/vvvq6mpSVdffbViYmJ0ww03aMyYMdq8ebNGjhx59gfjJ87VMR2uhnourFq1SjExMfqv//ov/elPf1JQUJCPKh+8Tz75RHfffbfS0tL02GOP6YYbblBpaalWrlyp1NRUX5cHAAAAP8Kc9wAA4IJVV1en3/3ud/rv//5vlZSUaPz48Vq0aJGKioo0ceJE2Ww2X5cI4ALn8Xj06aefatWqVfrrX/+qAwcOKC8vT3fddZduv/12RUdH+7pEAAAA+CnCewAAcFHYuHGjXnvtNa1atUqHDx9Wenq6Fi1apEWLFqmwsHBY3LELYHjo6urS2rVr9frrr+uNN95QVVWVRo8erUWLFunmm2/WpEmTfF0iAAAAhgHCewAAcNEpLi7WqlWr9Prrr2vnzp2KjY3VjBkzrGXChAnclQ9g0Dwej3bu3Kl169Zp7dq1+uijj+RyuXTFFVdYFwnz8vJ8XSYAAACGGcJ7AABwUTt06JDWrFmjDz/8UB9//LEaGxsVHx+vwsJCK8zPzc0lzAdg8Xg82rVrlxXWf/zxx2poaFBcXJyuvvpqXXPNNVq4cKGysrJ8XSoAAACGMcJ7AACA/5/H41FxcbE++ugjK5BrampSfHy8Jk2apIkTJ1oLD5YELh7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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task_graph = TaskGraph.load_taskgraph('../task_example/xgboost_trade.yaml')\n",
    "task_graph.draw(show='ipynb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features used for XGBoost algorithm are prepared in the `node_technical_indicator` node, where `cuIndicator` module is used to compute the technical indicators in the GPU for all the stock symbols. `node_xgboost_strategy` is the node that is used to compute the trading signals from the stock technical indicators. Each of the gQuant node is implemented by overwriting \"columns_setup\" and \"process\" methods of the Node base class. Please refer to [customize nodes notebook](https://github.com/rapidsai/gQuant/blob/master/notebooks/05_customize_nodes.ipynb) for details. Following is the source code for \"XGBoostStrategyNode\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class XGBoostStrategyNode(Node):\n",
      "    \"\"\"\n",
      "    This is the Node used to compute trading signal from XGBoost Strategy.\n",
      "    It requires the following conf fields:\n",
      "        \"train_date\": a date string of \"Y-m-d\" format. All the data points\n",
      "        before this date is considered as training, otherwise as testing. If\n",
      "        not provided, all the data points are considered as training.\n",
      "        \"xgboost_parameters\": a dictionary of any legal parameters for XGBoost\n",
      "        models. It overwrites the default parameters used in the process method\n",
      "        \"no_feature\": specifying a list of columns in the input dataframe that\n",
      "        should NOT be considered as training features.\n",
      "        \"target\": the column that is considered as \"target\" in machine learning\n",
      "        algorithm\n",
      "    It requires the \"datetime\" column for spliting the data points and adds a\n",
      "    new column \"signal\" to be used for backtesting.\n",
      "    The detailed computation steps are listed in the process method's docstring\n",
      "    \"\"\"\n",
      "\n",
      "    def columns_setup(self):\n",
      "        self.required = {'datetime': 'date'}\n",
      "        self.retention = self.conf['no_feature']\n",
      "        self.retention['signal'] = 'float64'\n",
      "\n",
      "    def process(self, inputs):\n",
      "        \"\"\"\n",
      "        The process is doing following things:\n",
      "            1. split the data into training and testing based on provided\n",
      "               conf['train_date']. If it is not provided, all the data is\n",
      "               treated as training data.\n",
      "            2. train a XGBoost model based on the training data\n",
      "            3. Make predictions for all the data points including training and\n",
      "               testing.\n",
      "            4. From the prediction of returns, compute the trading signals that\n",
      "               can be used in the backtesting.\n",
      "        Arguments\n",
      "        -------\n",
      "         inputs: list\n",
      "            list of input dataframes.\n",
      "        Returns\n",
      "        -------\n",
      "        dataframe\n",
      "        \"\"\"\n",
      "        dxgb_params = {\n",
      "                'max_depth':         8,\n",
      "                'max_leaves':        2 ** 8,\n",
      "                'tree_method':       'gpu_hist',\n",
      "                'objective':         'reg:squarederror',\n",
      "                'grow_policy':       'lossguide',\n",
      "        }\n",
      "        num_of_rounds = 100\n",
      "        if 'xgboost_parameters' in self.conf:\n",
      "            dxgb_params.update(self.conf['xgboost_parameters'])\n",
      "        input_df = inputs[0]\n",
      "        model_df = input_df\n",
      "        if 'train_date' in self.conf:\n",
      "            train_date = datetime.datetime.strptime(self.conf['train_date'],  # noqa: F841, E501\n",
      "                                                    '%Y-%m-%d')\n",
      "            model_df = model_df.query('datetime<@train_date')\n",
      "        train_cols = set(model_df.columns) - set(\n",
      "            self.conf['no_feature'].keys())\n",
      "        train_cols = list(train_cols - set([self.conf['target']]))\n",
      "        train = model_df[train_cols]\n",
      "        target = model_df[self.conf['target']]\n",
      "        dmatrix = xgb.DMatrix(train, label=target)\n",
      "        bst = xgb.train(dxgb_params, dmatrix,\n",
      "                        num_boost_round=num_of_rounds)\n",
      "        # make inferences\n",
      "        infer_dmatrix = xgb.DMatrix(input_df[train_cols])\n",
      "        prediction = cudf.Series(bst.predict(infer_dmatrix),\n",
      "                                 nan_as_null=False).astype('float64')\n",
      "        signal = compute_signal(prediction)\n",
      "        signal = cudf.Series(signal, index=input_df.index)\n",
      "        input_df['signal'] = signal\n",
      "        # remove the bad datapints\n",
      "        input_df = input_df.dropna()\n",
      "        remaining = list(self.conf['no_feature'].keys()) + ['signal']\n",
      "        return input_df[remaining]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import inspect\n",
    "from gquant.plugin_nodes import XGBoostStrategyNode\n",
    "\n",
    "print(inspect.getsource(XGBoostStrategyNode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Trading Strategy Performance\n",
    "Similar to tensorflow, gQuant graph is evaluated by specifying the output nodes and input nodes replacement. We first look at the column result from data preparation node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output columns of node node_technical_indicator:\n",
      "{'BO_BA_b1_10': 'float64',\n",
      " 'BO_BA_b2_10': 'float64',\n",
      " 'CH_OS_10_20': 'float64',\n",
      " 'SHIFT_-1': 'float64',\n",
      " 'asset': 'int64',\n",
      " 'close': 'float64',\n",
      " 'datetime': 'date',\n",
      " 'high': 'float64',\n",
      " 'indicator': 'int32',\n",
      " 'low': 'float64',\n",
      " 'open': 'float64',\n",
      " 'returns': 'float64',\n",
      " 'volume': 'float64'}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "task_graph.build()\n",
    "print('Output columns of node node_technical_indicator:')\n",
    "pprint(task_graph['node_technical_indicator'].output_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It adds the columns \"BO_BA_b1_10\", \"BO_BA_b2_10\", 'CH_OS_10_20\" as features and \"SHFIT_-1\" as the target, which is the return of next day. A good feature should be the one that provides highest information about the next day return. In the case we have no prior information about it,\n",
    "we can compute as many features as we like and leave it to the XGBoost to find the right combination of those features. \n",
    "\n",
    "Evaluate the leaf nodes of the backtesting graph by gQuant `run` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id:node_sort process time:0.143s\n",
      "id:node_addReturn process time:0.446s\n",
      "id:node_addIndicator process time:0.051s\n",
      "id:node_volumeMean process time:0.109s\n",
      "id:node_renameMeanVolume process time:0.001s\n",
      "id:node_leftMergeMeanVolume process time:2.698s\n",
      "id:node_maxReturns process time:0.024s\n",
      "id:node_renameMaxReturn process time:0.001s\n",
      "id:node_leftMergeMaxReturn process time:0.028s\n",
      "id:node_minReturns process time:0.024s\n",
      "id:node_renameMinReturn process time:0.001s\n",
      "id:node_leftMergeMinReturn process time:0.036s\n",
      "id:node_filterValue process time:0.332s\n",
      "id:node_dropColumns process time:0.008s\n",
      "id:node_sort2 process time:0.060s\n",
      "id:node_technical_indicator process time:3.803s\n",
      "id:node_xgboost_strategy process time:5.160s\n",
      "id:node_backtest process time:0.006s\n",
      "id:node_training_df process time:0.203s\n",
      "id:node_portOpt2 process time:0.032s\n",
      "id:node_sharpe_training process time:0.001s\n",
      "id:node_cumlativeReturn_training process time:2.228s\n",
      "id:node_testing_df process time:0.061s\n",
      "id:node_portOpt1 process time:0.025s\n",
      "id:node_sharpe_testing process time:0.001s\n",
      "id:node_cumlativeReturn_testing process time:2.452s\n"
     ]
    }
   ],
   "source": [
    "\n",
    "action = \"load\" if os.path.isfile('./.cache/node_csvdata.hdf5') else \"save\"\n",
    "outlist = ['node_sharpe_training','node_cumlativeReturn_training', 'node_sharpe_testing', 'node_cumlativeReturn_testing']\n",
    "replace_spec={'node_filterValue': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
    "                                            {\"column\": \"returns_max\", \"max\": max_rate},\n",
    "                                            {\"column\": \"returns_min\", \"min\": min_rate}]},\n",
    "              'node_csvdata': {action: True}}\n",
    "o_gpu = task_graph.run(\n",
    "            outputs=outlist + ['node_sort2'],\n",
    "            replace=replace_spec, profile=True)\n",
    "cached_sort = o_gpu[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to organized the plot results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50de025275284986b59b9b002d1285f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the function to format the plots\n",
    "def plot_figures(o):\n",
    "    # format the figures\n",
    "    figure_width = '1200px'\n",
    "    figure_height = '400px'\n",
    "    sharpe_number = o[0]\n",
    "    cum_return_train = o[1]\n",
    "    cum_return_train.layout.height = figure_height\n",
    "    cum_return_train.layout.width = figure_width\n",
    "    cum_return_train.title = 'Training P & L %.3f' % (sharpe_number)\n",
    "    sharpe_number = o[2]\n",
    "    cum_return_test = o[3]\n",
    "    cum_return_test.layout.height = figure_height\n",
    "    cum_return_test.layout.width = figure_width\n",
    "    cum_return_test.title = 'Testing P & L %.3f' % (sharpe_number)\n",
    "\n",
    "    return widgets.VBox([cum_return_train, cum_return_test])\n",
    "plot_figures(o_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, 3 feautres is way too little here. gQuant implmented 36 technical indicators. We can change the configuration of node_technical_indicator node to include more features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "chaikin_para0 = 10\n",
    "chaikin_para1 = 20\n",
    "bollinger_para = 10\n",
    "macd_para0 = 2\n",
    "macd_para1 = 3\n",
    "rsi_para0 = 5\n",
    "atr_para0 = 10\n",
    "sod_para = 2\n",
    "mflow_para = 3\n",
    "findex_para = 5\n",
    "adis_para = 5\n",
    "ccindex_para = 5\n",
    "bvol_para = 3\n",
    "vindex_para = 3\n",
    "mindex_para0 = 10\n",
    "mindex_para1 = 15\n",
    "tindex_para0 = 5\n",
    "tindex_para1 = 10\n",
    "emove_para = 5\n",
    "cc_para = 15\n",
    "kchannel_para = 10\n",
    "indicator_conf = {\n",
    "    \"indicators\": [\n",
    "        {\"function\": \"port_chaikin_oscillator\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\", \"volume\"],\n",
    "         \"args\": [chaikin_para0, chaikin_para1]\n",
    "        },\n",
    "        {\"function\": \"port_bollinger_bands\",\n",
    "         \"columns\": [\"close\"],\n",
    "         \"args\": [bollinger_para],\n",
    "         \"outputs\": [\"b1\", \"b2\"]\n",
    "        },\n",
    "        {\"function\": \"port_macd\",\n",
    "         \"columns\": [\"close\"],\n",
    "         \"args\": [macd_para0, macd_para1],\n",
    "         \"outputs\": [\"MACDsign\", \"MACDdiff\"]\n",
    "        },\n",
    "        {\"function\": \"port_relative_strength_index\",\n",
    "         \"columns\": [\"high\", \"low\"],\n",
    "         \"args\": [rsi_para0],\n",
    "        },\n",
    "        {\"function\": \"port_average_true_range\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\"],\n",
    "         \"args\": [atr_para0],\n",
    "        },\n",
    "        {\"function\": \"port_stochastic_oscillator_k\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\"],\n",
    "         \"args\": [],\n",
    "        },\n",
    "        {\"function\": \"port_stochastic_oscillator_d\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\"],\n",
    "         \"args\": [sod_para],\n",
    "        },\n",
    "        {\"function\": \"port_money_flow_index\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\", \"volume\"],\n",
    "         \"args\": [mflow_para],\n",
    "        },\n",
    "        {\"function\": \"port_force_index\",\n",
    "         \"columns\": [\"close\", \"volume\"],\n",
    "         \"args\": [findex_para],\n",
    "        },\n",
    "        {\"function\": \"port_ultimate_oscillator\",\n",
    "         \"columns\": [\"high\",\"low\",\"close\"],\n",
    "         \"args\": [],\n",
    "        },\n",
    "        {\"function\": \"port_accumulation_distribution\",\n",
    "         \"columns\": [\"high\",\"low\",\"close\",\"volume\"],\n",
    "         \"args\": [adis_para],\n",
    "        },\n",
    "        {\"function\": \"port_commodity_channel_index\",\n",
    "         \"columns\": [\"high\",\"low\",\"close\"],\n",
    "         \"args\": [ccindex_para],\n",
    "        },\n",
    "        {\"function\": \"port_on_balance_volume\",\n",
    "         \"columns\": [\"close\", \"volume\"],\n",
    "         \"args\": [bvol_para],\n",
    "        },\n",
    "        {\"function\": \"port_vortex_indicator\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\"],\n",
    "         \"args\": [vindex_para],\n",
    "        },\n",
    "         {\"function\": \"port_kst_oscillator\",\n",
    "         \"columns\": [\"close\"],\n",
    "         \"args\": [3, 4, 5, 6, 7, 8, 9, 10],\n",
    "        },\n",
    "        {\"function\": \"port_mass_index\",\n",
    "         \"columns\": [\"high\", \"low\"],\n",
    "         \"args\": [mindex_para0, mindex_para1],\n",
    "        },\n",
    "        {\"function\": \"port_true_strength_index\",\n",
    "         \"columns\": [\"close\"],\n",
    "         \"args\": [tindex_para0, tindex_para1],\n",
    "        },\n",
    "        {\"function\": \"port_ease_of_movement\",\n",
    "         \"columns\": [\"high\", \"low\", \"volume\"],\n",
    "         \"args\": [emove_para],\n",
    "        },\n",
    "        {\"function\": \"port_coppock_curve\",\n",
    "         \"columns\": [\"close\"],\n",
    "         \"args\": [cc_para],\n",
    "        },\n",
    "        {\"function\": \"port_keltner_channel\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\"],\n",
    "         \"args\": [kchannel_para],\n",
    "         \"outputs\": [\"KelChD\", \"KelChM\", \"KelChU\"]\n",
    "        },\n",
    "        {\"function\": \"port_ppsr\",\n",
    "         \"columns\": [\"high\", \"low\", \"close\"],\n",
    "         \"args\": [],\n",
    "         \"outputs\": [\"PP\", \"R1\", \"S1\", \"R2\", \"S2\", \"R3\", \"S3\"]\n",
    "        },\n",
    "        {\"function\": \"port_shift\",\n",
    "         \"columns\": [\"returns\"],\n",
    "         \"args\": [-1]\n",
    "        }        \n",
    "    ],\n",
    "    \"remove_na\": True\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the backtesting again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id:node_technical_indicator process time:4.281s\n",
      "id:node_xgboost_strategy process time:9.202s\n",
      "id:node_backtest process time:0.004s\n",
      "id:node_training_df process time:0.060s\n",
      "id:node_portOpt2 process time:0.028s\n",
      "id:node_sharpe_training process time:0.001s\n",
      "id:node_cumlativeReturn_training process time:2.107s\n",
      "id:node_testing_df process time:0.054s\n",
      "id:node_portOpt1 process time:0.027s\n",
      "id:node_sharpe_testing process time:0.001s\n",
      "id:node_cumlativeReturn_testing process time:2.119s\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "079b407b3c4c41428c44ba81eda3a78c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "replace_spec['node_technical_indicator'] = {\"conf\": indicator_conf}\n",
    "replace_spec['node_sort2'] = {\"load\": cached_sort}\n",
    "o_gpu = task_graph.run(\n",
    "            outputs=outlist,\n",
    "            replace=replace_spec, profile=True)\n",
    "plot_figures(o_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get Sharpe Raio of `1.93` in the testing dataset, not bad!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strategy parameter search\n",
    "Quantitative analyst usually need to explore different parameters for their trading strategy. The exploration process is an iterative process. gQuant help to speed up this by allowing using cached dataframe and evaluating the sub-graphs.\n",
    "\n",
    "To find the optimal technical indicator parameters for this XGBoost strategy, we build a wiget to search the parameter interactively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2c589837c6a4f9c8849b545d6f2ed04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(IntRangeSlider(value=(10, 20), continuous_update=False, description='Chaikin', m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotutils\n",
    "plotutils.getXGBoostWidget(replace_spec, task_graph, outlist, plot_figures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "In this notebook, we demoed how to use gQuant to backtest XGBoost trading strategy. It is convenient and efficient to use indicator node from the gQuant to compute features for all the stocks in the dataset in the GPU. The XGBoost training are computed in the GPU, so we can get the results quickly. This example shows the XGBoost algorithm's power in finding trading signals. We can achieve close to 2 raw Sharpe ratio in the testing time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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